December 22, 2021
What is data integrity
Data Quality versus Data Integrity
Data Quality refers to the traits that determine the ability of information to suit an intended function, such as planning, decision making, and operations.
Data Quality is evaluated based on whether it is complete, unique, timely, valid, and consistent.
The data has to be:
- Complete – representative of a large percentage of the total amount of data being processed.
- Unique – free of redundancy and extraneous entries.
- Timely – up-to-date as possible.
- Valid – syntax and structure that is defined by the needs of the business.
- Consistent – represented and stored in a standard acceptable framework.
Data Integrity is determined by factors such as data accuracy, validity, and consistency over time.
- Data Accuracy – data values stored for an object are the correct values.
- Validity – delivery of clean and clear data to the programs, applications and services using it.
- Consistency – each user sees a reliable view of the data, including visible changes made by the user’s own transactions and transactions of other users.
It refers to the lack of unwanted data changes between two updates or modifications to data records. Data integrity is the complete opposite of data corruption, which causes information to be inadequate in meeting intended data needs.
In order to achieve Data Integrity the organization needs to focus on integration, quality, location intelligence, and enrichment. With respect to integration, the data must be effortlessly integrated into a single perspective that can provide organizations with rapid and increased visibility. The data also needs to adhere to acceptable quality standards – complete, unique, valid, timely, and consistent. By utilizing location intelligence, this adds a layer of richness and complexity that provides greater insight and analytics which makes the data much more actionable across the organization. And finally, the enrichment of the data by leveraging external sources of information helps add perspective and meaning.
Why is Data Integrity Important and What are the Benefits
Data Integrity and data quality should both be viewed as top business priorities across Life Sciences organizations. As a result, it’s critical to ensure data quality while also retaining data integrity. Businesses are exposed to a number of dangers if they do not preserve data integrity or ensure data quality. In decision-making and other data-driven business processes, bad data leads to more human error and could potentially impact the profitability of the organization. When businesses talk about data integrity vs. data quality, there needs to be a concerted effort to instill a culture of quality and compliance as well as adherence to industry leading practices through proper training across the organization. .
Data Integrity is important to the organization as it ensures and secures the searchability and traceability of the organization’s data back to its original source as well as establish and showcase the compliance posture of the organization. Maintaining the integrity of data and ensuring its completeness and security is also essential for the organization to have confidence that the data is reliable and has not been compromised.
As the organization collects more and more data it becomes an even higher priority to secure and maintain the integrity of that data. Without integrity and accuracy, the data is worthless. Data loss, corrupted or compromised data can considerably damage the business – as is evident from the many instances across the globe relating to data loss and compromised data.
Data Quality and Data Integrity are different aspects of data. Data Quality refers to whether or not the data is complete, unique, timely, valid, and consistent, Data Integrity refers to data accuracy, validity, and consistency over time. Both should be viewed as a top priority by the business. The organization is exposed to a significant vulnerabilities if it does not preserve Data Integrity or ensure Data Quality.
Data Integrity is important because it ensures and secures data so that it is searchable and traceable. The benefit that Data Integrity brings to the organization is confidence that the data is complete and secure and has not been compromised. Without the integrity and accuracy, the organizations data is ineffective.
Why it Matters to You
Data Integrity is important to Life Sciences organizations because it ensures confidence that the data is complete and secure, and has not been compromised. The quality of data effects product quality and patient safety, so it is critical to safeguard data integrity.
In this blog we discuss:
- The difference between Data Quality and Data Integrity.
- Key attributes of both Data Quality and Data Integrity.
- Why Data Integrity and Data Quality are important to the organization.
- The key benefits of the organization of Data Integrity.
For over 25 years, Astrix has been a market-leader in dedicated digital transformation & staffing services for science-based businesses. Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate. Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond. We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.